A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production

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	A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production
其他题名	A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production
	Pang Xinfu 1; Gao Liang 1; Pan Quanke 1; Tian Weihua 2; Yu Shengping 3
	2017
发表期刊	JOURNAL OF CENTRAL SOUTH UNIVERSITY
ISSN	2095-2899
卷号	24 期号:2 页码:467-477
摘要	A Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.
其他摘要	A Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.
关键词	APPROXIMATE SUBGRADIENT METHODS HYBRID FLOWSHOP ALGORITHM STEEL TIME OPTIMIZATION CONVERGENCE SYSTEM steelmaking-refining-continuous casting Lagrangian relaxation (LR) approximate subgradient optimization
收录类别	CSCD
语种	英语
资助项目	[National Natural Science Foundation of China] ; [Postdoctoral Science Foundation of China] ; [Liaoning Province Education Administration, China]
CSCD记录号	CSCD:5986060
引用统计	被引频次：7[CSCD] [CSCD记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/142248
专题	中国科学院金属研究所
作者单位	1.Huazhong University Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China 2.中国科学院金属研究所 3.东北大学
推荐引用方式 GB/T 7714	Pang Xinfu,Gao Liang,Pan Quanke,et al. A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production[J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY,2017,24(2):467-477.
APA	Pang Xinfu,Gao Liang,Pan Quanke,Tian Weihua,&Yu Shengping.(2017).A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production.JOURNAL OF CENTRAL SOUTH UNIVERSITY,24(2),467-477.
MLA	Pang Xinfu,et al."A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production".JOURNAL OF CENTRAL SOUTH UNIVERSITY 24.2(2017):467-477.